Update app.py

app.py

@@ -15,33 +15,14 @@ from Text2List import text_to_list   # Converts a text string into a list of wor
 from convert2list import convert_to_list   # Converts processed text into a structured list.
 from processDoubles import process_doubles   # Handles repeated words or numbers in speech recognition output.
 from replaceWords import replace_words   # Replaces specific words in the recognized text with alternatives.
+from highPassFiler import high_pass_filter
+form WaveletDenoise import wavelet_denoise
+fomr applyWienerFilter import apply_wiener_filter
 
 # Initialize ASR model pipeline
 asr_model = pipeline("automatic-speech-recognition", model="cdactvm/w2v-bert-punjabi")
 
 
-# Function to apply a high-pass filter
-def high_pass_filter(audio, sr, cutoff=300):
-    nyquist = 0.5 * sr
-    normal_cutoff = cutoff / nyquist
-    b, a = butter(1, normal_cutoff, btype='high', analog=False)
-    filtered_audio = lfilter(b, a, audio)
-    return filtered_audio
-
-# Function to apply wavelet denoising
-def wavelet_denoise(audio, wavelet='db1', level=1):
-    import pywt
-    coeffs = pywt.wavedec(audio, wavelet, mode='per')
-    sigma = np.median(np.abs(coeffs[-level])) / 0.5
-    uthresh = sigma * np.sqrt(2 * np.log(len(audio)))
-    coeffs[1:] = [pywt.threshold(i, value=uthresh, mode='soft') for i in coeffs[1:]]
-    return pywt.waverec(coeffs, wavelet, mode='per')
-
-# Function to apply a Wiener filter for noise reduction
-def apply_wiener_filter(audio):
-    return wiener(audio)
-
-
 # Function to handle speech recognition
 def recognize_speech(audio_file):
     audio, sr = librosa.load(audio_file, sr=16000)